Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Abstract

Two pre-processes for Automatic Identification System (AIS) trajectories commonly reported in the maritime knowledge discovery literature are trajectory simplification and event identification. Both pre-processes reduce storage and computational expenses by reducing the number of data points to be used in an analysis. This paper presents an event identification and trajectory simplification method based on behaviour identification and translation. Trajectory segments deemed to correspond to coastal or ocean navigation are translated into equivalent passage plan segments; a succinct description of the movements and behaviour of the ship. As a trajectory simplification method, it provides two main advantages over commonly used trajectory simplification methods: more meaningful simplified trajectories with better encoding of basic behaviours and the possibility to retain interesting behaviours in full resolution. As an event identification method, it is capable of differentiating between normal ocean or coastal navigating behaviour and complex or interesting behaviour, such as pilotage, reaction to a traffic conflict, or an involuntary deviation from the passage plan.

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

“…Among them, the running speed of the algorithm is signifcantly improved after the region division, and the compression rate of the algorithm can be adjusted by parameters. In general, the length loss of the compressed trajectory is mainly concentrated in the regions where the ship turns frequently or the waters are complex [28], and the proposed algorithm also extracts iteratively for these regions. Te length loss is mainly concentrated in the regions where the vessel turns frequently, or the waters are complex [28], and the target algorithm also repeatedly extracts these regions.…”

“…In addition to the fact that the compression results are greatly afected by parameters, the sliding window algorithm only considers the operating mechanism of the trajectory points in the window, which may result in a greater degree of trajectory deformation before and after compression. Many researchers have improved this problem [28][29][30][31]. Sánchez-Heres and Sánchez [28] proposed a trajectory simplifcation algorithm based on behavior recognition for an equivalent passage plan (EPP).…”

“…Many researchers have improved this problem [28][29][30][31]. Sánchez-Heres and Sánchez [28] proposed a trajectory simplifcation algorithm based on behavior recognition for an equivalent passage plan (EPP). First, the behavior of the vessel was divided into three types: stop, sail, and turn, and then, the trajectory data corresponded to these three behaviors.…”

Review and classification of trajectory summarisation algorithms: From compression to segmentation

“…Others combine preserve criteria in several passes and with a combination of conditionals, such as Feng et al 115 with speed, distance and angle calculated through the SED projection; or Gao and Shi 94 with angle and SED. Sa´nchez-Heres 78 does something similar to compress the straight lines but keep the turns.…”

An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation

“…For instance, Arguedas et al (2017) proposed to compute the Hausdorff distance to cluster together similar vessel behaviours assisting in producing maritime traffic representations from historical AIS data. This distance has also been introduced to enable event identification in the maritime knowledge discovery (Sánchez-Heres, 2019). The OWD was adopted to assist in automatically recognizing the vessel motion patterns from massive historical AIS data (Ma et al, 2014).…”